Download or read book Multi agent Reinforcement Learning Approaches for Distributed Job Shop Scheduling Problems written by Thomas Gabel and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Learning in Cooperative Multi Agent Systems written by Thomas Gabel and published by Sudwestdeutscher Verlag Fur Hochschulschriften AG. This book was released on 2009-09 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a distributed system, a number of individually acting agents coexist. In order to achieve a common goal, coordinated cooperation between the agents is crucial. Many real-world applications are well-suited to be formulated in terms of spatially or functionally distributed entities. Job-shop scheduling represents one such application. Multi-agent reinforcement learning (RL) methods allow for automatically acquiring cooperative policies based solely on a specification of the desired joint behavior of the whole system. However, the decentralization of the control and observation of the system among independent agents has a significant impact on problem complexity. The author Thomas Gabel addresses the intricacy of learning and acting in multi-agent systems by two complementary approaches. He identifies a subclass of general decentralized decision-making problems that features provably reduced complexity. Moreover, he presents various novel model-free multi-agent RL algorithms that are capable of quickly obtaining approximate solutions in the vicinity of the optimum. All algorithms proposed are evaluated in the scope of various established scheduling benchmark problems.
Download or read book Generic Multi Agent Reinforcement Learning Approach for Flexible Job Shop Scheduling written by Schirin Bär and published by Springer Nature. This book was released on 2022-10-01 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The production control of flexible manufacturing systems is a relevant component that must go along with the requirements of being flexible in terms of new product variants, new machine skills and reaction to unforeseen events during runtime. This work focuses on developing a reactive job-shop scheduling system for flexible and re-configurable manufacturing systems. Reinforcement Learning approaches are therefore investigated for the concept of multiple agents that control products including transportation and resource allocation.
Download or read book A Cooperative Hierarchical Deep Reinforcement Learning Based Multi Agent Method for Distributed Job Shop Scheduling Problem with Random Job Arrivals written by Jiang-Ping Huang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed manufacturing has been an important trend in the industrial field, in which the production cost can be reduced through the cooperation among factories. In the real production, the random job arrivals are regular for the enterprises with daily delivered production tasks. In the paper, Distributed Job-shop Scheduling Problem (DJSP) with random job arrivals is studied. The distributed characteristics and the uncertain disturbance raise higher demands on the responsiveness and the self-adaptiveness of the scheduling method. To meet the scheduling requirements, a hierarchical Deep Reinforcement Learning (DRL) based multi-agent method Agentin is presented where the assigning agent (Agenta) and the sequencing agent (Agents) are respectively designed for job allocation and job sequencing, and they share the system information and extract the features they need independently. Agenta and Agents are both based on the specially-designed DQN framework, which has a variable threshold probability in the training stage, and it can balance the exploitation and exploration in the model training. For Agenta and Agents, two Markov Decision Process (MDP) formulations are established with elaborately-explored state features, rules-based action spaces and objective-oriented reward functions. Based on 1350 different production instances, the independent utility tests prove the effectiveness of the independent agents and the importance of the cooperation among the agents. The comparison test with the related algorithms validates the effectiveness of the integrated multi-agent method.
Download or read book Optimization and Learning written by Bernabé Dorronsoro and published by Springer Nature. This book was released on 2020-02-15 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the Third International Conference on Optimization and Learning, OLA 2020, held in Cádiz, Spain, in February 2020. The 23 full papers were carefully reviewed and selected from 55 submissions. The papers presented in the volume focus on the future challenges of optimization and learning methods, identifying and exploiting their synergies,and analyzing their applications in different fields, such as health, industry 4.0, games, logistics, etc.
Download or read book Multi Agent Coordination written by Arup Kumar Sadhu and published by John Wiley & Sons. This book was released on 2020-12-01 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium Improving convergence speed of multi-agent Q-learning for cooperative task planning Consensus Q-learning for multi-agent cooperative planning The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning A modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.
Download or read book Intelligent Quality Systems written by Duc T. Pham and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the tenn quality does not have a precise and universally accepted definition, its meaning is generally well understood: quality is what makes the difference between success and failure in a competitive world. Given the importance of quality, there is a need for effective quality systems to ensure that the highest quality is achieved within given constraints on human, material or financial resources. This book discusses Intelligent Quality Systems, that is quality systems employing techniques from the field of Artificial Intelligence (AI). The book focuses on two popular AI techniques, expert or knowledge-based systems and neural networks. Expert systems encapsulate human expertise for solving difficult problems. Neural networks have the ability to learn problem solving from examples. The aim of the book is to illustrate applications of these techniques to the design and operation of effective quality systems. The book comprises 8 chapters. Chapter 1 provides an introduction to quality control and a general discussion of possible AI-based quality systems. Chapter 2 gives technical information on the key AI techniques of expert systems and neural networks. The use of these techniques, singly and in a combined hybrid fonn, to realise intelligent Statistical Process Control (SPC) systems for quality improvement is the subject of Chapters 3-5. Chapter 6 covers experimental design and the Taguchi method which is an effective technique for designing quality into a product or process. The application of expert systems and neural networks to facilitate experimental design is described in this chapter.
Download or read book Agent Based Optimization written by Ireneusz Czarnowski and published by Springer. This book was released on 2012-12-14 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents a collection of original research works by leading specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of the agent-based optimization. The book consists of eight chapters covering examples of application of the multi-agent paradigm and respective customized tools to solve difficult optimization problems arising in different areas such as machine learning, scheduling, transportation and, more generally, distributed and cooperative problem solving.
Download or read book Multiagent Scheduling written by Alessandro Agnetis and published by Springer Science & Business Media. This book was released on 2014-01-31 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scheduling theory has received a growing interest since its origins in the second half of the 20th century. Developed initially for the study of scheduling problems with a single objective, the theory has been recently extended to problems involving multiple criteria. However, this extension has still left a gap between the classical multi-criteria approaches and some real-life problems in which not all jobs contribute to the evaluation of each criterion. In this book, we close this gap by presenting and developing multi-agent scheduling models in which subsets of jobs sharing the same resources are evaluated by different criteria. Several scenarios are introduced, depending on the definition and the intersection structure of the job subsets. Complexity results, approximation schemes, heuristics and exact algorithms are discussed for single-machine and parallel-machine scheduling environments. Definitions and algorithms are illustrated with the help of examples and figures.
Download or read book Holonic and Multi Agent Systems for Manufacturing written by Vladimir Marik and published by Springer Science & Business Media. This book was released on 2007-08-22 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the Third International Conference on Industrial Applications of Holonic and Multi-Agent Systems held in September 2007. The 39 full papers were selected from among 63 submissions. They are organized into topical sections covering theoretical and methodological issues, algorithms and technologies, implementation and validation, applications, and supply chain management.
Download or read book Introduction to Scheduling written by Yves Robert and published by CRC Press. This book was released on 2009-11-18 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Full of practical examples, Introduction to Scheduling presents the basic concepts and methods, fundamental results, and recent developments of scheduling theory. With contributions from highly respected experts, it provides self-contained, easy-to-follow, yet rigorous presentations of the material.The book first classifies scheduling problems and
Download or read book Reinforcement Learning for Job shop Scheduling written by Wei Zhang and published by . This book was released on 1996 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation studies applying reinforcement learning algorithms to discover good domain-specific heuristics automatically for job-shop scheduling. It focuses on the NASA space shuttle payload processing problem. The problem involves scheduling a set of tasks to satisfy a set of temporal and resource constraints while also seeking to minimize the total length (makespan) of the schedule. The approach described in the dissertation employs a repair-based scheduling problem space that starts with a critical-path schedule and incrementally repairs constraint violations with the goal of finding a short conflict-free schedule. The temporal difference (TD) learning algorithm TD([lambda]) is applied to train a neural network to learn a heuristic evaluation function for choosing repair actions over schedules. This learned evaluation function is used by a one-step lookahead search procedure to nd solutions to new scheduling problems. Several important issues that affect the success and the efficiency of learning have been identified and deeply studied. These issues include schedule representation, network architectures, and learning strategies. A number of modifications to the TD([lambda]) algorithm are developed to improve learning performance. Learning is investigated based on both hand-engineered features and raw features. For learning from raw features, a time-delay neural network architecture is developed to extract features from irregular-length schedules. The learning approach is evaluated on synthetic problems and on problems from a NASA space shuttle payload processing task. The evaluation function is learned on small problems and then applied to solve larger problems. Both learning-based schedulers (using hand-engineered features and raw features respectively) perform better than the best existing algorithm for this task--Zweben's iterative repair method. It is important to understand why TD learning works in this application. Several performance measures are employed to investigate learning behavior. We verified that TD learning works properly in capturing the evaluation function. It is concluded that TD learning along with a set of good features and a proper neural network is the key to this success. The success shows that reinforcement learning methods have the potential for quickly finding high-quality solutions to other combinatorial optimization problems.
Download or read book Agents and Multi agent Systems Technologies and Applications 2023 written by Gordan Jezic and published by Springer Nature. This book was released on 2023-05-27 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights new trends and challenges in research on agents and the new digital and knowledge economy. It includes papers on business process management, agent-based modeling and simulation and anthropic-oriented computing that were originally presented at the 17th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications (KES-AMSTA 2023), held in Rome, Italy, in June 14–16, 2023. The respective papers cover topics such as software agents, multi-agent systems, agent modeling, mobile and cloud computing, big data analysis, business intelligence, artificial intelligence, social systems, computer embedded systems and nature-inspired manufacturing, all of which contribute to the modern digital economy.
Download or read book Chemical Production Scheduling written by Christos T. Maravelias and published by Cambridge University Press. This book was released on 2021-05-06 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand common scheduling as well as other advanced operational problems with this valuable reference from a recognized leader in the field. Beginning with basic principles and an overview of linear and mixed-integer programming, this unified treatment introduces the fundamental ideas underpinning most modeling approaches, and will allow you to easily develop your own models. With more than 150 figures, the basic concepts and ideas behind the development of different approaches are clearly illustrated. Addresses a wide range of problems arising in diverse industrial sectors, from oil and gas to fine chemicals, and from commodity chemicals to food manufacturing. A perfect resource for engineering and computer science students, researchers working in the area, and industrial practitioners.
Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Download or read book Dynamic Scheduling in Large scale Manufacturing Processing Systems Using Multi agent Reinforcement Learning written by Shuhui Qu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Scheduling in manufacturing plays an essential role in building smart manufacturing from multiple points of view, including social, economic, and environmental. Optimal scheduling, or the allocation of jobs with different requirements for a manufacturing processing system to meet various objectives, has been discussed for several decades. However, advanced scheduling methods in modern processing systems have not significantly improved, nor have they been widely adopted by staff working on manufacturing production lines despite extensive research conducted into scheduling. Most traditional scheduling methods require statistical assumptions, which cannot support operations for a dynamic and stochastic modern processing system. In addition, most proposed scheduling methods are not sufficiently scalable for managing real-world, large-scale processing systems. To address these limitations, we focus on the dynamic scheduling approach, which involves scheduling real-time events in large-scale modern manufacturing systems, from a data-driven perspective. We implement reinforcement learning (RL) to learn adaptive, scalable, and optimal dynamic scheduling policies, since RL can learn the underlying processing system's patterns and adaptively make allocation decisions based on real-time job and server measurements. The direct application of existing RL methods on the scheduling problem in such large-scale processing systems is impractical and undesired due to the extremely high computational complexity of learning a good scheduling policy. This thesis presents a practical and systematic computational framework that integrates RL with existing expert knowledge at three levels: (1) System-level planning. The planning procedure characterizes the processing system by the nominal feasible region of the scheduling problem. (2) Algorithm-level design. The design of the algorithm in RL is carefully selected as the index-policy-based, multi-agent RL, significantly reducing control policy search complexity. (3) Learning-level demonstration. During the learning process of RL, the existing expert knowledge is used as a demonstration to increase search efficiency and stabilize the RL learning process. We conduct various experiments in both real factory scenarios and simulated environments to evaluate the performance of the framework on processing system scheduling problems. The effectiveness of the proposed index-policy-based, multi-agent reinforcement learning (MARL) method is evidenced by its performance over traditional dynamic scheduling methods, with a linear computational time complexity in regard to the number of machines and job classes.
Download or read book From batch size 1 to serial production Adaptive robots for scalable and flexible production systems written by Mohamad Bdiwi and published by Frontiers Media SA. This book was released on 2023-05-24 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt:
